This assumes that $P(A) \not= 0$ and $P(B) \not= 0$. This is a simple statement of Bayes' Theorem. If we assume that $P(B)$ can be partitioned into a series mutually exclusive possibilities which sum to $P(B)$ then we can generated the extended form:

This may be easier to interpet using the concrete example of a Bayesian classifier, where $P(B)$ represents the probability that a specific word will occur in a message, and $P(B|A_i)$ represents the probability that the word will occur in a message of a specific category $A_i$, on the assumption that categories are complete (i.e. each message is always of exactly one of the predefined categories). It is therefore easy to see that summing all of the $P(B|A_i)$ will yield $P(B)$ since they are mutually exclusive and cover all the possible ways that $P(B)$ can occur.

Naive Bayesian Classifier

The process of determining the class of a piece of text involves splitting it up into tokens (words) and calculating the probability of each word occurring in each class of message. We assume $n$ classifications of messages, $C_1, C_2, ... C_n$, in the examples below and consider the effect of a word $W$.

The classifier is naive because it assumes the contribution of each token to the classification of the message is independent. Cases where tokens occurring together provide a much stronger indication than either token appearing individually may not be suitable for the naive approach.

Classification based on a word

Using the extended form of Bayes' Theorem, we can specify the probability that a message containing a particular word $W$ will be given a particular classification $C_i$:

This depends partly on the ratio of messages with particular classifications $P(C_i)$ which makes it a biased classifier (i.e. it makes assumptions about the distribution of incoming messages before even looking at them). However, some classifiers make the simplifying assumption that all classifications are initially equally likely, which yields:

This allows the probability of a given word classifying the message correctly in terms of the relative frequencies of that word in the different categories, which is easily acquired through suitable training.

Combining words

Of course, messages may be made up of many tokens and each has its own contribution to make to the overall classification. Combining the probabilities from the previous section requires more applications of Bayes' Theorem and some other techniques. For the sake of this illustration we assume there are only two classifications $C_1$ and $C_2$, and only two tokens $W_a$ and $W_b$ that have been found in the message. We shall generalise the approach after this simple example.

So, we are interested in determining the probability that the message can be classified as a particular classification, we shall take $C_1$ for this example. We already have $P(C_1|W_a)$ and $P(C_1|W_b)$ as derived in the previous section. As a starting point for the derivation, consider a simple application of Bayes' Theorem to calculate the probability of the message being classified as $C_1$ given that both tokens have been found:

Clearly this isn't suitable as each token's occurrence in messages is only recorded independently (because this is a naive classifier) so neither $P(W_a \cap W_b)$ nor $P(W_a \cap W_b|C_1)$ is available. We can simplify this based on the assumption that the tokens are conditionally independent based on knowing whether the message is spam. This allows us to use the equality:

This is an improvement, but now we need to remove the denominator term as well. We can do this via normalisation. This uses the fact that the message containing both tokens must be classified as either $C_1$ or $C_2$ — that is to say their probabilities must sum to 1. We can then restate these probabilities using Bayes' Theorem and then multiply through by the shared denominator to obtain an equivalent form for it:

Please forgive the slightly loose use of notation, there are a few too many dimensions over which to iterate for clarity.

One slight simplification to note results from the fact that $P(C_i)$ is presumably determined by dividing a number of trained messages by the total number of messages trained. Let $N_{C_i}$ indicate the number of messages trained in category $C_i$, $N$ indicate the number of messages trained overall and $N_{C_i}(W_a)$ indicate the number of messages containing token $W_a$ that were trained in category $C_i$. Thus the equation above becomes:

Where $x$ is the total number of words. This version keeps the values relatively large so should hopefully reduce problems with floating point underflow (although may be susceptible to overflow if the number of tokens becomes excessive).

Two-category case

A common case is that there are two categories — for example, this is the case for email spam detection. In this case it can be tempting to simplify the above equation using the fact that $P(C_1) = (1 - P(C_2))$. However, this is not as effective as it seems as you would also need to assume that $P(W_i|C_1) = (1 - P(W_i|C_2))$ to achieve any significant simplifcation. However, this is clearly not the case — just because the word “drugs” occurs in 20% of spam email, for example, it doesn't follow that it occurs in 80% of non-spam.

Precision issues

Since many of the probabilities for particular words may be quite low once a large corpus of messages has been analysed, the product of large numbers of them can lead to underflow if floating point representations are used. One solution to this is to limit the analysis to a small number of “most interesting” words - this has other performance improvements as well.

Another technique which can also be used is to perform the multiplications in the log space and use addition instead of multiplication. This uses the identity: